Overview

Dataset statistics

Number of variables14
Number of observations5309
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory580.8 KiB
Average record size in memory112.0 B

Variable types

Numeric13
Categorical1

Alerts

df_index is highly correlated with type and 1 other fieldsHigh correlation
type is highly correlated with df_index and 4 other fieldsHigh correlation
volatile acidity is highly correlated with typeHigh correlation
chlorides is highly correlated with type and 1 other fieldsHigh correlation
free sulfur dioxide is highly correlated with type and 1 other fieldsHigh correlation
total sulfur dioxide is highly correlated with df_index and 2 other fieldsHigh correlation
density is highly correlated with chlorides and 1 other fieldsHigh correlation
alcohol is highly correlated with densityHigh correlation
df_index is highly correlated with type and 1 other fieldsHigh correlation
type is highly correlated with df_index and 2 other fieldsHigh correlation
volatile acidity is highly correlated with typeHigh correlation
residual sugar is highly correlated with densityHigh correlation
free sulfur dioxide is highly correlated with total sulfur dioxideHigh correlation
total sulfur dioxide is highly correlated with df_index and 2 other fieldsHigh correlation
density is highly correlated with residual sugar and 1 other fieldsHigh correlation
alcohol is highly correlated with densityHigh correlation
df_index is highly correlated with typeHigh correlation
type is highly correlated with df_index and 2 other fieldsHigh correlation
chlorides is highly correlated with typeHigh correlation
free sulfur dioxide is highly correlated with total sulfur dioxideHigh correlation
total sulfur dioxide is highly correlated with type and 1 other fieldsHigh correlation
density is highly correlated with alcoholHigh correlation
alcohol is highly correlated with densityHigh correlation
df_index is highly correlated with type and 3 other fieldsHigh correlation
type is highly correlated with df_index and 4 other fieldsHigh correlation
fixed acidity is highly correlated with df_index and 3 other fieldsHigh correlation
volatile acidity is highly correlated with df_index and 1 other fieldsHigh correlation
residual sugar is highly correlated with densityHigh correlation
chlorides is highly correlated with type and 1 other fieldsHigh correlation
free sulfur dioxide is highly correlated with total sulfur dioxideHigh correlation
total sulfur dioxide is highly correlated with df_index and 2 other fieldsHigh correlation
density is highly correlated with fixed acidity and 2 other fieldsHigh correlation
sulphates is highly correlated with chloridesHigh correlation
alcohol is highly correlated with fixed acidity and 1 other fieldsHigh correlation
df_index has unique values Unique
citric acid has 135 (2.5%) zeros Zeros

Reproduction

Analysis started2021-10-01 08:00:10.413195
Analysis finished2021-10-01 08:00:45.837643
Duration35.42 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct5309
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3250.84498
Minimum0
Maximum6496
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size41.6 KiB
2021-10-01T13:30:45.995193image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile317.4
Q11579
median3240
Q34927
95-th percentile6173.6
Maximum6496
Range6496
Interquartile range (IQR)3348

Descriptive statistics

Standard deviation1897.569775
Coefficient of variation (CV)0.5837158604
Kurtosis-1.239922084
Mean3250.84498
Median Absolute Deviation (MAD)1674
Skewness-0.002673063995
Sum17258736
Variance3600771.05
MonotonicityStrictly increasing
2021-10-01T13:30:46.322773image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
44161
 
< 0.1%
44121
 
< 0.1%
44111
 
< 0.1%
44101
 
< 0.1%
44081
 
< 0.1%
44071
 
< 0.1%
44061
 
< 0.1%
44041
 
< 0.1%
44031
 
< 0.1%
Other values (5299)5299
99.8%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
61
< 0.1%
91
< 0.1%
101
< 0.1%
111
< 0.1%
121
< 0.1%
131
< 0.1%
ValueCountFrequency (%)
64961
< 0.1%
64951
< 0.1%
64921
< 0.1%
64911
< 0.1%
64901
< 0.1%
64891
< 0.1%
64881
< 0.1%
64871
< 0.1%
64861
< 0.1%
64851
< 0.1%

type
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size41.6 KiB
2
3955 
1
1354 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
23955
74.5%
11354
 
25.5%

Length

2021-10-01T13:30:46.506283image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-01T13:30:46.600076image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
23955
74.5%
11354
 
25.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

fixed acidity
Real number (ℝ≥0)

HIGH CORRELATION

Distinct106
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.217263138
Minimum3.8
Maximum15.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.6 KiB
2021-10-01T13:30:46.718529image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum3.8
5-th percentile5.6
Q16.4
median7
Q37.7
95-th percentile9.8
Maximum15.9
Range12.1
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.319173097
Coefficient of variation (CV)0.1827802411
Kurtosis4.600489437
Mean7.217263138
Median Absolute Deviation (MAD)0.6
Skewness1.651702401
Sum38316.45
Variance1.740217659
MonotonicityNot monotonic
2021-10-01T13:30:46.927104image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.8278
 
5.2%
6.6268
 
5.0%
6.4245
 
4.6%
7229
 
4.3%
6.9225
 
4.2%
6.7211
 
4.0%
7.2202
 
3.8%
7.1200
 
3.8%
6.5196
 
3.7%
6.2176
 
3.3%
Other values (96)3079
58.0%
ValueCountFrequency (%)
3.81
 
< 0.1%
3.91
 
< 0.1%
4.22
 
< 0.1%
4.43
 
0.1%
4.51
 
< 0.1%
4.62
 
< 0.1%
4.76
 
0.1%
4.89
 
0.2%
4.96
 
0.1%
526
0.5%
ValueCountFrequency (%)
15.91
< 0.1%
15.62
< 0.1%
15.51
< 0.1%
151
< 0.1%
14.31
< 0.1%
14.21
< 0.1%
141
< 0.1%
13.81
< 0.1%
13.71
< 0.1%
13.51
< 0.1%

volatile acidity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct188
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3435543417
Minimum0
Maximum1.58
Zeros8
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size41.6 KiB
2021-10-01T13:30:47.134821image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.16
Q10.23
median0.3
Q30.41
95-th percentile0.68
Maximum1.58
Range1.58
Interquartile range (IQR)0.18

Descriptive statistics

Standard deviation0.1686638667
Coefficient of variation (CV)0.4909379571
Kurtosis2.856768376
Mean0.3435543417
Median Absolute Deviation (MAD)0.08
Skewness1.491758728
Sum1823.93
Variance0.02844749992
MonotonicityNot monotonic
2021-10-01T13:30:47.332160image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.28231
 
4.4%
0.26218
 
4.1%
0.24217
 
4.1%
0.27188
 
3.5%
0.25186
 
3.5%
0.22182
 
3.4%
0.23177
 
3.3%
0.2177
 
3.3%
0.3168
 
3.2%
0.21163
 
3.1%
Other values (178)3402
64.1%
ValueCountFrequency (%)
08
 
0.2%
0.082
 
< 0.1%
0.0851
 
< 0.1%
0.091
 
< 0.1%
0.16
 
0.1%
0.1054
 
0.1%
0.119
 
0.2%
0.1153
 
0.1%
0.1228
0.5%
0.1252
 
< 0.1%
ValueCountFrequency (%)
1.581
< 0.1%
1.332
< 0.1%
1.241
< 0.1%
1.1851
< 0.1%
1.181
< 0.1%
1.131
< 0.1%
1.1151
< 0.1%
1.11
< 0.1%
1.091
< 0.1%
1.071
< 0.1%

citric acid
Real number (ℝ≥0)

ZEROS

Distinct89
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3187944999
Minimum0
Maximum1.66
Zeros135
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size41.6 KiB
2021-10-01T13:30:47.545960image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.04
Q10.24
median0.31
Q30.4
95-th percentile0.56
Maximum1.66
Range1.66
Interquartile range (IQR)0.16

Descriptive statistics

Standard deviation0.1470978256
Coefficient of variation (CV)0.461418957
Kurtosis2.591333435
Mean0.3187944999
Median Absolute Deviation (MAD)0.07
Skewness0.4862039596
Sum1692.48
Variance0.02163777031
MonotonicityNot monotonic
2021-10-01T13:30:47.780296image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3264
 
5.0%
0.32240
 
4.5%
0.28236
 
4.4%
0.49232
 
4.4%
0.26204
 
3.8%
0.34202
 
3.8%
0.29198
 
3.7%
0.31187
 
3.5%
0.24184
 
3.5%
0.27179
 
3.4%
Other values (79)3183
60.0%
ValueCountFrequency (%)
0135
2.5%
0.0131
 
0.6%
0.0244
 
0.8%
0.0326
 
0.5%
0.0434
 
0.6%
0.0523
 
0.4%
0.0625
 
0.5%
0.0727
 
0.5%
0.0836
 
0.7%
0.0934
 
0.6%
ValueCountFrequency (%)
1.661
 
< 0.1%
1.231
 
< 0.1%
16
0.1%
0.991
 
< 0.1%
0.911
 
< 0.1%
0.881
 
< 0.1%
0.861
 
< 0.1%
0.822
 
< 0.1%
0.812
 
< 0.1%
0.81
 
< 0.1%

residual sugar
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct316
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.051798832
Minimum0.6
Maximum65.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.6 KiB
2021-10-01T13:30:48.045588image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile1.1
Q11.8
median2.7
Q37.5
95-th percentile14.4
Maximum65.8
Range65.2
Interquartile range (IQR)5.7

Descriptive statistics

Standard deviation4.501714492
Coefficient of variation (CV)0.8911111946
Kurtosis7.025523086
Mean5.051798832
Median Absolute Deviation (MAD)1.5
Skewness1.705777895
Sum26820
Variance20.26543337
MonotonicityNot monotonic
2021-10-01T13:30:48.339800image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2200
 
3.8%
1.6199
 
3.7%
1.4194
 
3.7%
1.8192
 
3.6%
1.2173
 
3.3%
2.2156
 
2.9%
1.5149
 
2.8%
2.1148
 
2.8%
1.9148
 
2.8%
1.7148
 
2.8%
Other values (306)3602
67.8%
ValueCountFrequency (%)
0.61
 
< 0.1%
0.77
 
0.1%
0.825
 
0.5%
0.935
 
0.7%
0.953
 
0.1%
177
1.5%
1.051
 
< 0.1%
1.1126
2.4%
1.153
 
0.1%
1.2173
3.3%
ValueCountFrequency (%)
65.81
< 0.1%
31.61
< 0.1%
26.051
< 0.1%
23.51
< 0.1%
22.61
< 0.1%
221
< 0.1%
20.82
< 0.1%
20.71
< 0.1%
20.41
< 0.1%
20.31
< 0.1%

chlorides
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct214
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05666811076
Minimum0.009
Maximum0.611
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.6 KiB
2021-10-01T13:30:48.612070image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.009
5-th percentile0.028
Q10.038
median0.047
Q30.066
95-th percentile0.104
Maximum0.611
Range0.602
Interquartile range (IQR)0.028

Descriptive statistics

Standard deviation0.03686076168
Coefficient of variation (CV)0.6504674533
Kurtosis48.3849435
Mean0.05666811076
Median Absolute Deviation (MAD)0.011
Skewness5.348422871
Sum300.851
Variance0.001358715752
MonotonicityNot monotonic
2021-10-01T13:30:48.832517image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.036165
 
3.1%
0.044160
 
3.0%
0.042158
 
3.0%
0.046155
 
2.9%
0.04152
 
2.9%
0.047148
 
2.8%
0.048143
 
2.7%
0.05141
 
2.7%
0.038141
 
2.7%
0.034137
 
2.6%
Other values (204)3809
71.7%
ValueCountFrequency (%)
0.0091
 
< 0.1%
0.0122
 
< 0.1%
0.0131
 
< 0.1%
0.0144
 
0.1%
0.0153
 
0.1%
0.0165
 
0.1%
0.0175
 
0.1%
0.0188
0.2%
0.0197
0.1%
0.0213
0.2%
ValueCountFrequency (%)
0.6111
< 0.1%
0.611
< 0.1%
0.4671
< 0.1%
0.4641
< 0.1%
0.4221
< 0.1%
0.4152
< 0.1%
0.4142
< 0.1%
0.4131
< 0.1%
0.4031
< 0.1%
0.4011
< 0.1%

free sulfur dioxide
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct135
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.04426446
Minimum1
Maximum289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.6 KiB
2021-10-01T13:30:49.044047image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q116
median28
Q341
95-th percentile61
Maximum289
Range288
Interquartile range (IQR)25

Descriptive statistics

Standard deviation17.8159925
Coefficient of variation (CV)0.5929914686
Kurtosis9.51294538
Mean30.04426446
Median Absolute Deviation (MAD)12
Skewness1.362895506
Sum159505
Variance317.4095889
MonotonicityNot monotonic
2021-10-01T13:30:49.246055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6150
 
2.8%
29144
 
2.7%
26134
 
2.5%
15132
 
2.5%
24127
 
2.4%
17124
 
2.3%
34123
 
2.3%
31123
 
2.3%
23121
 
2.3%
28115
 
2.2%
Other values (125)4016
75.6%
ValueCountFrequency (%)
12
 
< 0.1%
22
 
< 0.1%
350
 
0.9%
442
 
0.8%
5111
2.1%
5.51
 
< 0.1%
6150
2.8%
782
1.5%
877
1.5%
980
1.5%
ValueCountFrequency (%)
2891
< 0.1%
146.51
< 0.1%
138.51
< 0.1%
1311
< 0.1%
1281
< 0.1%
1241
< 0.1%
122.51
< 0.1%
118.51
< 0.1%
1121
< 0.1%
1101
< 0.1%

total sulfur dioxide
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct276
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean114.1770578
Minimum6
Maximum440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.6 KiB
2021-10-01T13:30:49.565441image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile19
Q175
median116
Q3154
95-th percentile205.6
Maximum440
Range434
Interquartile range (IQR)79

Descriptive statistics

Standard deviation56.76962257
Coefficient of variation (CV)0.497206914
Kurtosis-0.299444838
Mean114.1770578
Median Absolute Deviation (MAD)39
Skewness0.06199830104
Sum606166
Variance3222.790046
MonotonicityNot monotonic
2021-10-01T13:30:49.833723image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11154
 
1.0%
11350
 
0.9%
11449
 
0.9%
12248
 
0.9%
9848
 
0.9%
12846
 
0.9%
11043
 
0.8%
10143
 
0.8%
10443
 
0.8%
11743
 
0.8%
Other values (266)4842
91.2%
ValueCountFrequency (%)
62
 
< 0.1%
74
 
0.1%
811
 
0.2%
914
0.3%
1024
0.5%
1122
0.4%
1226
0.5%
1325
0.5%
1430
0.6%
1528
0.5%
ValueCountFrequency (%)
4401
< 0.1%
366.51
< 0.1%
3441
< 0.1%
3131
< 0.1%
307.51
< 0.1%
3031
< 0.1%
2941
< 0.1%
2891
< 0.1%
2821
< 0.1%
2781
< 0.1%

density
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct996
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9945350235
Minimum0.98711
Maximum1.03898
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.6 KiB
2021-10-01T13:30:50.044159image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.98711
5-th percentile0.98984
Q10.9922
median0.99466
Q30.99678
95-th percentile0.999172
Maximum1.03898
Range0.05187
Interquartile range (IQR)0.00458

Descriptive statistics

Standard deviation0.002968020321
Coefficient of variation (CV)0.002984329612
Kurtosis8.697280558
Mean0.9945350235
Median Absolute Deviation (MAD)0.00226
Skewness0.6652792038
Sum5279.98644
Variance8.809144625 × 10-6
MonotonicityNot monotonic
2021-10-01T13:30:50.254596image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.99259
 
1.1%
0.997259
 
1.1%
0.99853
 
1.0%
0.992853
 
1.0%
0.997652
 
1.0%
0.993250
 
0.9%
0.996850
 
0.9%
0.993450
 
0.9%
0.996249
 
0.9%
0.996648
 
0.9%
Other values (986)4786
90.1%
ValueCountFrequency (%)
0.987111
< 0.1%
0.987131
< 0.1%
0.987221
< 0.1%
0.98741
< 0.1%
0.987422
< 0.1%
0.987462
< 0.1%
0.987581
< 0.1%
0.987741
< 0.1%
0.987791
< 0.1%
0.987941
< 0.1%
ValueCountFrequency (%)
1.038981
< 0.1%
1.01031
< 0.1%
1.003691
< 0.1%
1.00321
< 0.1%
1.003152
< 0.1%
1.002951
< 0.1%
1.002891
< 0.1%
1.00262
< 0.1%
1.002421
< 0.1%
1.002411
< 0.1%

pH
Real number (ℝ≥0)

Distinct108
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.224330382
Minimum2.72
Maximum4.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.6 KiB
2021-10-01T13:30:50.453773image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2.72
5-th percentile2.98
Q13.11
median3.21
Q33.33
95-th percentile3.5
Maximum4.01
Range1.29
Interquartile range (IQR)0.22

Descriptive statistics

Standard deviation0.1601821357
Coefficient of variation (CV)0.04967919435
Kurtosis0.443686454
Mean3.224330382
Median Absolute Deviation (MAD)0.11
Skewness0.3938210819
Sum17117.97
Variance0.0256583166
MonotonicityNot monotonic
2021-10-01T13:30:50.666405image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.16156
 
2.9%
3.22154
 
2.9%
3.14146
 
2.8%
3.15144
 
2.7%
3.2142
 
2.7%
3.24141
 
2.7%
3.18139
 
2.6%
3.19136
 
2.6%
3.12130
 
2.4%
3.17128
 
2.4%
Other values (98)3893
73.3%
ValueCountFrequency (%)
2.721
 
< 0.1%
2.742
 
< 0.1%
2.771
 
< 0.1%
2.792
 
< 0.1%
2.83
 
0.1%
2.821
 
< 0.1%
2.833
 
0.1%
2.841
 
< 0.1%
2.856
0.1%
2.868
0.2%
ValueCountFrequency (%)
4.012
< 0.1%
3.92
< 0.1%
3.851
< 0.1%
3.821
< 0.1%
3.811
< 0.1%
3.82
< 0.1%
3.791
< 0.1%
3.782
< 0.1%
3.772
< 0.1%
3.762
< 0.1%

sulphates
Real number (ℝ≥0)

HIGH CORRELATION

Distinct111
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5332586174
Minimum0.22
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.6 KiB
2021-10-01T13:30:50.888529image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.22
5-th percentile0.35
Q10.43
median0.51
Q30.6
95-th percentile0.79
Maximum2
Range1.78
Interquartile range (IQR)0.17

Descriptive statistics

Standard deviation0.1497686145
Coefficient of variation (CV)0.2808554979
Kurtosis8.628636631
Mean0.5332586174
Median Absolute Deviation (MAD)0.08
Skewness1.81276042
Sum2831.07
Variance0.02243063789
MonotonicityNot monotonic
2021-10-01T13:30:51.093978image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5211
 
4.0%
0.46196
 
3.7%
0.54192
 
3.6%
0.44184
 
3.5%
0.48170
 
3.2%
0.38164
 
3.1%
0.47161
 
3.0%
0.52161
 
3.0%
0.49159
 
3.0%
0.45157
 
3.0%
Other values (101)3554
66.9%
ValueCountFrequency (%)
0.221
 
< 0.1%
0.231
 
< 0.1%
0.254
 
0.1%
0.263
 
0.1%
0.2710
 
0.2%
0.2812
 
0.2%
0.2912
 
0.2%
0.323
0.4%
0.3131
0.6%
0.3244
0.8%
ValueCountFrequency (%)
21
 
< 0.1%
1.981
 
< 0.1%
1.951
 
< 0.1%
1.621
 
< 0.1%
1.611
 
< 0.1%
1.591
 
< 0.1%
1.561
 
< 0.1%
1.363
0.1%
1.341
 
< 0.1%
1.331
 
< 0.1%

alcohol
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct111
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.54958812
Minimum8
Maximum14.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.6 KiB
2021-10-01T13:30:51.306410image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile9
Q19.5
median10.4
Q311.4
95-th percentile12.7
Maximum14.9
Range6.9
Interquartile range (IQR)1.9

Descriptive statistics

Standard deviation1.186768661
Coefficient of variation (CV)0.1124943123
Kurtosis-0.539477493
Mean10.54958812
Median Absolute Deviation (MAD)0.9
Skewness0.5463285558
Sum56007.76333
Variance1.408419855
MonotonicityNot monotonic
2021-10-01T13:30:51.499115image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.5288
 
5.4%
9.4260
 
4.9%
9.2205
 
3.9%
10204
 
3.8%
10.5193
 
3.6%
11175
 
3.3%
9.8174
 
3.3%
9.3169
 
3.2%
10.4165
 
3.1%
10.2157
 
3.0%
Other values (101)3319
62.5%
ValueCountFrequency (%)
82
 
< 0.1%
8.44
 
0.1%
8.510
 
0.2%
8.616
 
0.3%
8.749
 
0.9%
8.866
1.2%
8.958
1.1%
9136
2.6%
9.051
 
< 0.1%
9.1126
2.4%
ValueCountFrequency (%)
14.91
 
< 0.1%
14.21
 
< 0.1%
14.051
 
< 0.1%
1411
0.2%
13.93
 
0.1%
13.82
 
< 0.1%
13.75
0.1%
13.611
0.2%
13.566666671
 
< 0.1%
13.551
 
< 0.1%

quality
Real number (ℝ≥0)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7963835
Minimum3
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.6 KiB
2021-10-01T13:30:51.662162image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q15
median6
Q36
95-th percentile7
Maximum9
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8800401789
Coefficient of variation (CV)0.1518257339
Kurtosis0.2997344851
Mean5.7963835
Median Absolute Deviation (MAD)1
Skewness0.1506900433
Sum30773
Variance0.7744707165
MonotonicityNot monotonic
2021-10-01T13:30:51.785356image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
62317
43.6%
51750
33.0%
7854
 
16.1%
4204
 
3.8%
8149
 
2.8%
330
 
0.6%
95
 
0.1%
ValueCountFrequency (%)
330
 
0.6%
4204
 
3.8%
51750
33.0%
62317
43.6%
7854
 
16.1%
8149
 
2.8%
95
 
0.1%
ValueCountFrequency (%)
95
 
0.1%
8149
 
2.8%
7854
 
16.1%
62317
43.6%
51750
33.0%
4204
 
3.8%
330
 
0.6%

Interactions

2021-10-01T13:30:42.723871image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:11.542224image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:14.079536image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:16.513198image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:19.232301image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:21.702539image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:24.228062image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:26.680061image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:29.273898image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:31.845691image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:34.805775image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:37.563215image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:40.056880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:42.913364image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:11.721329image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:14.265604image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:16.699703image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:19.419641image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:21.892420image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:24.407332image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:26.985556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:29.451389image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:32.037193image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:34.983252image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:37.749716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:40.244963image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:43.089893image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:11.898853image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:14.448094image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:16.896304image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:19.603516image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:22.072791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:24.591840image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:27.197984image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:29.628915image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:32.221687image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:35.161750image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:37.934221image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:40.447419image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:43.287366image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:12.096942image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:14.639165image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:17.131678image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:19.801986image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:22.383073image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:24.790309image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:27.399484image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:29.849323image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:32.441138image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:35.408090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:38.141667image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:40.653214image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:43.470874image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:12.272473image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:14.837174image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:17.331350image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:19.987967image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:22.561588image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:24.975813image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:27.606825image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:30.030442image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:32.629595image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:35.624510image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:38.343130image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:40.900553image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:43.664356image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:12.452846image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:15.047608image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:17.526843image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:20.171477image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:22.747596image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:25.160319image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:27.794322image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:30.211767image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:32.942757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:35.847274image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:38.534618image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:41.083066image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:43.854847image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:12.633364image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:15.227163image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:17.835691image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:20.359936image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:22.930107image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:25.350808image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:27.982512image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:30.390470image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:33.243952image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:36.108547image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:38.717025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:41.273955image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:44.041346image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:12.831832image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:15.413593image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:18.029468image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:20.541451image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:23.130605image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:25.552268image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:28.169378image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:30.566973image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:33.517221image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:36.325965image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:38.904172image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:41.579138image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:44.217875image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:13.006623image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:15.583139image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:18.215971image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:20.719848image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:23.297124image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:25.723324image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:28.344351image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:30.737551image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:33.788495image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:36.507484image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:39.084903image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:41.749683image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:44.401382image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:13.313800image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:15.761660image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:18.406460image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:20.904720image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:23.504571image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:25.913850image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:28.529856image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:30.919031image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:34.057774image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:36.683015image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:39.272365image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:41.927209image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:44.583935image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:13.493286image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:15.950156image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:18.597949image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:21.090190image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:23.683711image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:26.097056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:28.712367image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:31.102681image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:34.238292image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:36.988472image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:39.470834image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:42.118698image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:44.780370image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:13.693749image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:16.143407image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:18.794050image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:21.315583image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:23.871243image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:26.287547image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:28.901901image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:31.302147image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:34.438754image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:37.176287image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:39.680273image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:42.316961image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:44.966909image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:13.886233image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:16.333923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:19.023436image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:21.512645image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:24.053720image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:26.493994image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:29.092349image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:31.636252image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:34.626288image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:37.373721image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:39.867349image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-01T13:30:42.545350image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-10-01T13:30:51.951909image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-01T13:30:52.265104image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-01T13:30:52.683950image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-01T13:30:53.101514image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-10-01T13:30:45.303969image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-10-01T13:30:45.694225image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indextypefixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
0027.00.270.3620.700.04545.0170.01.00103.000.458.86
1126.30.300.341.600.04914.0132.00.99403.300.499.56
2228.10.280.406.900.05030.097.00.99513.260.4410.16
3327.20.230.328.500.05847.0186.00.99563.190.409.96
4626.20.320.167.000.04530.0136.00.99493.180.479.66
5928.10.220.431.500.04428.0129.00.99383.220.4511.06
61028.10.270.411.450.03311.063.00.99082.990.5612.05
71128.60.230.404.200.03517.0109.00.99473.140.539.75
81227.90.180.371.200.04016.075.00.99203.180.6310.85
91326.60.160.401.500.04448.0143.00.99123.540.5212.47

Last rows

df_indextypefixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
5299648515.80.6100.111.80.06618.028.00.994833.550.6610.96
5300648617.20.0000.332.50.06834.0102.00.994143.270.7812.86
5301648716.60.7250.207.80.07329.079.00.997703.290.549.25
5302648816.30.5500.151.80.07726.035.00.993143.320.8211.66
5303648915.40.7400.091.70.08916.026.00.994023.670.5611.66
5304649016.30.5100.132.30.07629.040.00.995743.420.7511.06
5305649116.80.6200.081.90.06828.038.00.996513.420.829.56
5306649216.20.6000.082.00.09032.044.00.994903.450.5810.55
5307649515.90.6450.122.00.07532.044.00.995473.570.7110.25
5308649616.00.3100.473.60.06718.042.00.995493.390.6611.06